Localization in changing environments - estimation of a covariance matrix for the IDC algorithm

We (1999) have previously presented a new scan-matching algorithm based on the iterative dual correspondence (IDC) algorithm, which showed a good localization performance even in the case of severe changes in the environment. The problem with the IDC algorithm is that there is no good way to estimate the covariance matrix of the position estimate, thus prohibits an effective fusion with other position estimates from other sensors, e.g., by means of the Kalman filter. In this paper we present a new way to estimate the covariance matrix by estimating the Hessian matrix of the error function that is minimized by the IDC scan-matching algorithm. Simulation results show that the estimated covariance matrix correspond well to the real one.